Pixels and Principles: Ethics of AI Art | Melody Liu | TEDxNortheasternU
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Published 2024-03-27
This Talk prompts fundamental questions about human identity and uniqueness in a landscape where creative arts confront the threat of automation.
Melody Liu is a 2023 Northeastern University graduate with a degree in Computer Science and Business Administration, and is the co-founder of the Digital Illustration Association (DIA) at NU. She shared a simple yet powerful mission: to create a welcoming and open-minded community that embraces digital illustration in all its forms. Whether through drawing-parties, art challenges, or supportive critiques, Melody helped create a space where artists of all experience levels can connect with patrons and each other, fostering both personal and artistic growth. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at www.ted.com/tedx
All Comments (2)
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I appreciate that you got someone to speak who understands both the technical and art sides of the issue fairly well. That said, there are a few parts that bear clarification/rebuttal: Competition in relation to fair use: The speaker uses style as an example here, but copyright does not protect styles, only actual works. Anti-AI image modification processes: The claims made by the U of C and others are considered dubious at best by many in the AI community, in part because they require the altered images to make up a substantial portion of the training data, can (at least in some cases) be undone, and do perceptibly alter some images. Copyright registration: Technically speaking, all creative works are granted copyright in the US once published. Registration is required if someone wants to pursue legal action regarding a work, however. The speaker made it sound like unregistered works have no status at all, which is untrue. Permission/compensation: I’m skeptical of such a system working out, largely due to the prospects of civil disobedience (general consumers ignoring any restrictions), models developed in other jurisdictions, and the ability to generate models locally (large firms may have an advantage in regards to the latest and greatest models, but the increasing power of consumer-grade hardware will continue to expand what kinds of models can be built by individuals or small groups). The speaker seems to assume that generative models will be confined to large corporations, which is already untrue in some domains such as voice synthesis.
